Read Curtis L. Evan's new white paper, The Human Element in Business Analytics »

IIL Logo - International Institute for Learning
LinkedIn Newsletter | Join our Email List

New Course!

Generative AI for Project Management

Generative AI for Project Management shows you how to use multiple generative AI tools to start, plan, and manage either your own project or a generic case study.

This course is tool agnostic, so while you will experience working with different AI platforms, you will also learn the principles of how to utilize AI tools to optimize your time and your outcomes. Content includes up-to-date information on AI, hands-on practice, and higher-level thinking about AI.

Contact our team to check if you qualify for a discount or to discuss group pricing or training a team.

You can also email us at [email protected] or submit a request below.

AI can become a powerful ally but with one crucial caveat – understanding and interpreting its recommendations. In this webinar, we will explore the secrets of leveraging Generative AI effectively. It is a powerful tool waiting to be harnessed.

As project managers, we need to embrace transparency, interpret outputs, and lead our teams toward successful project outcomes. Let us step beyond the black box and shape the future of project management together!

Reviews from IIL Learners!

Frequently Asked Questions

AI, or Artificial Intelligence, is the ability of a computer or a robot controlled by a computer to do tasks that are usually done by humans because they require human intelligence and discernment. It's essentially about creating machines that can think, learn, and adapt.
AI learns primarily through algorithms in a process called Machine Learning. It involves analyzing and learning from data to find patterns or make decisions. The more data the AI is exposed to, the better it becomes at its task. This can be through supervised learning (learning from labeled data), unsupervised learning (finding patterns in data), or reinforcement learning (learning through trial and error).

While AI is a broad concept that refers to machines capable of performing tasks that seem intelligent, Machine Learning (ML) is a subset of AI focused on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention. ML is the method through which we achieve many AI functions, using statistical techniques to enable machines to improve at tasks with experience.

Generative AI is a type of AI that can generate new content, whether it’s text, images, or music. It learns from a vast amount of existing material and then uses that knowledge to create original, plausible new outputs. It’s like teaching a computer to be creative based on patterns it has learned from existing works.

Currently, AI cannot "think" like humans in a comprehensive way. AI systems excel at processing large amounts of data and recognizing patterns within this data much faster than humans. However, they lack consciousness, emotions, and the ability to understand context in the way humans do. AI's decision-making is based on data and algorithms, and it does not possess the human aspects of thought, such as intuition and reasoning based on emotional intelligence.

There are mainly two types of AI: Narrow AI and General AI.

Narrow AI, also known as Weak AI, is designed for specific tasks such as voice recognition or image recognition and is the type of AI predominantly seen today (e.g., Siri, Alexa).

General AI, also known as Strong AI, refers to systems that possess the ability to perform any intellectual task that a human can do. General AI is still a theoretical concept and not yet achieved.

AI has numerous applications in daily life. Personal assistants like Siri and Alexa help in performing tasks through voice commands. Recommendation systems on platforms like Netflix or Amazon personalize user experience by suggesting products or content. Autonomous vehicles use AI to interpret sensory data to identify appropriate navigation paths. AI is also used in fraud detection, medical diagnoses, and even in smart home devices for energy efficiency.

Ethical considerations in AI include issues like privacy, bias, transparency, and job displacement. Ensuring AI systems respect user privacy and data security is vital. AI systems can also reflect or amplify biases present in their training data, so it's important to develop AI in a way that is fair and unbiased. Transparency in AI processes helps in building trust and understanding its decision-making. Moreover, as AI automates tasks, there are concerns about job displacement, highlighting the need for policies to manage economic and social impacts.

Healthcare: AI is used for diagnostic procedures, personalized medicine, and drug discovery.

Finance: AI aids in fraud detection, algorithmic trading, and personalized customer service.

Transportation: Self-driving cars and optimization of logistics and delivery services.

Retail: AI provides personalized shopping experiences and inventory management.

Entertainment: AI curates personalized content recommendations in streaming services and video games.

Search Pages on

Discover courses, online conferences, white papers, videos, webinars and much more….